The School of Computer Science is pleased to present…
Roadside Unit (RSU) Misbehaviour Detection in VANET Using Trust Model
MSc Thesis Defense by: Lavanya Nagaraju
Date: Tuesday, August 13th, 2024
Time: 12:00 PM
Location: Essex Hall Room 122
Numerous concerns exist across the various components in the ever-evolving field of Vehicular Networks. Still, the one that is specifically crucial in managing network traffic and data exchange is Roadside Units (RSUs). These devices ensure robust and reliable communication between various components of Vehicular Ad Hoc Networks (VANETs). They are also susceptible to misbehaviours that could compromise the entire network's integrity and functionality. Given the critical nature of these, it becomes crucial to detect the misbehaviour. Hence, in this research, we have developed a trust-based model for detecting RSU misbehaviour in VANETs. Firstly, we reviewed the existing trust models and identified their limitations. While some of the trust models in VANETs had limitations in the complex trust calculations, others assumed that RSUs were trusted and had the trust model performed by the vehicles. To address these limitations, we have proposed a method that involves designing a combined trust model, which is a combination of global and recommendation-based trust models where direct and indirect interactions are used for evaluating trust and sent to the Trust Authority (TA), which aggregates these trust assessments from individual RSUs to adjust the global trust scores of RSUs dynamically. The effectiveness of this model is evaluated by carrying out simulations and test scenarios with varying numbers of misbehaving RSUs to assess the model's responsiveness and reliability. This research will provide a trust-based framework capable of accurately detecting RSU misbehaviour.
Internal Reader: Dr. Shaoquan Jiang
External Reader: Dr. Mitra Mirhassani
Advisor: Dr. Ikjot Saini
Chair: Dr. Dan Wu